Thomas Reindl

Sarmad Hanif, D. F. R. Melo, Mehdi Maasoumy et al.

This paper proposes a robust demand-side control algorithm in a smart grid environment for heating, ventilation and air conditioning (HVAC) systems. A robust model predictive control (RMPC) scheme in a receding horizon fashion is deployed, which optimizes electricity cost and capacity market participation of the HVAC system, while satisfying comfort and operational constraints of the building and utility, respectively. Thermal load uncertainties experienced by the HVAC system are included to perform a realistic assessment of the developed controller. The National Electricity Market of Singapore (NEMS) is used as a case study and the developed RMPC scheme is tested for various price signals and scenarios. Numerical simulation results show the effectiveness of the developed framework to be readily adopted by utilities -- interested in realizing a grid-friendly and economicaly eficient demand response (DR) strategy.

Katayoun Rahbar, Mohammad R. Vedady Moghadam, Sanjib Kumar Panda et al.

This paper investigates the energy management problem for multiple self-interested users, each with renewable energy generation as well as both the fixed and controllable loads, that all share a common energy storage system (ESS). The self-interested users are willing to sell/buy energy to/from the shared ESS if they can achieve lower energy costs compared to the case of no energy trading while preserving their privacy e.g. sharing only limited information with a central controller. Under this setup, we propose an iterative algorithm by which the central controller coordinates the charging/discharging values to/from the shared ESS by all users such that their individual energy costs reduce at the same time. For performance benchmark, the case of cooperative users that all belong to the same entity is considered, where they share all the required information with the central controller so as to minimize their total energy cost. Finally, the effectiveness of our proposed algorithm in simultaneously reducing users' energy costs is shown via simulations based on realistic system data of California, US.

Katayoun Rahbar, Mohammad R. Vedady Moghadam, Sanjib Kumar Panda et al.

Energy storage systems (ESSs) are essential components of the future smart grid to smooth out the fluctuating output of renewable energy generators. However, installing large number of ESSs for individual energy consumers may not be practically implementable, due to both the space limitation and high investment cost. As a result, in this paper, we study the energy management problem of multiple users with renewable energy sources and a single shared ESS. To solve this problem, we propose an algorithm that jointly optimizes the energy charged/discharged to/from the shared ESS given a profit coefficient set that specifies the desired proportion of the total profit allocated to each user, subject to practical constraints of the system. We conduct simulations based on the real data from California, US, and show that the shared ESS can potentially increase the total profit of all users by 10% over the case that users own individual small-scale ESSs with no energy sharing.